classic-ID3-DecisionTree 2.0.3

ID3 is a Machine Learning Decision Tree Classification Algorithm that uses two methods to build the model. The two methods are Information Gain and Gini Index.

ID3 Decision Tree Algorithm

ID3 is a Machine Learning Decision Tree Classification Algorithm that uses two methods to build the model. The two methods are Information Gain and Gini Index.

• Version 1.0.0 - Information Gain Only
• Version 2.0.0 - Gini Index added
• Version 2.0.1 - Documentation Sorted
• Version 2.0.2 - All Sorted

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Installation

Install directly from my PyPi

pip install classic-ID3-DecisionTree

Or Clone the Repository and install

python3 setup.py install

Parameters

* X_train

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The Training Set array consisting of Features.

* y_train

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The Training Set array consisting of Outcome.

* dataset

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The Entire DataSet.

Attributes

* DecisionTreeClassifier()

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Initialise the instance of Decision Tree Classifier class.

* add_features(dataset, result_col_name)

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Add the features to the model by sending the dataset. The model will fetch the column features. The second parameter is the column name of outcome array.

* information_gain(X_train, y_train)

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To build the decision tree using Information Gain

* gini_index(X_train, y_train)

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To build the decision tree using Gini Index

* predict(y_test)

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Predict the Test Set Results

Documentation

1. Install the package

pip install classic-ID3-DecisionTree

2. Import the library

from classic_ID3_decision_tree import DecisionTreeClassifier

3. Create an object for Decision Tree Classifier class

id3 = DecisionTreeClassifier()

4. Add Column Features to the model

id3.add_features(dataset, result_col_name)

5. Build the Decision Tree Model using Information Gain

id3.information_gain(X_train, y_train)

OR

5. Build the Decision Tree Model using Gini Index

id3.gini_index(X_train, y_train)

6. Predict the Test Set Results

y_pred = id3.predict(X_test)

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Example Code

0. Download the dataset

Download dataset from here

1. Import the dataset and Preprocess

• import numpy as np
• import matplotlib.pyplot as plt
• import pandas as pd

• dataset = pd.read_csv('house-votes-84.csv')
• rawdataset = pd.read_csv('house-votes-84.csv')
• party = {'republican':0, 'democrat':1}
• vote = {'y':1, 'n':0, '?':0}

• for col in dataset.columns:
  • if col != 'party':
    • dataset[col] = dataset[col].map(vote)
• dataset['party'] = dataset['party'].map(party)

• X = dataset.iloc[:, 1:17].values
• y = dataset.iloc[:, 0].values

• from sklearn.model_selection import KFold
• kf = KFold(n_splits=5)

• for train_index, test_index in kf.split(X,y):
  • X_train, X_test = X[train_index], X[test_index]
  • y_train, y_test = y[train_index], y[test_index]

2. Use the ID3 Library

• from classic_ID3_decision_tree import DecisionTreeClassifier
• id3 = DecisionTreeClassifier()
• id3.add_features(dataset, 'party')
• print(id3.features)

• id3.information_gain(X_train, y_train)
• OR
• id3.gini_index(X_train, y_train)
• y_pred = ig.predict(X_test)

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Footnotes

You can find the code at my Github.

Connect with me on Social Media

• https://www.github.com/safir72347
• https://www.linkedin.com/in/safir72347/
• https://www.instagram.com/safir_12_10/